def handle(self, *args, **options):
# 1) Get all the sounds that have remixes
cursor = connection.cursor()
cursor.execute("""
SELECT
src.from_sound_id AS from,
src.to_sound_id AS to,
snd.created AS created
FROM
sounds_sound_sources src,
sounds_sound snd
WHERE
src.to_sound_id = snd.id
ORDER BY
snd.created ASC
""")
# 2) Create directed graph
dg = nx.DiGraph()
for row in cursor:
dg.add_edge(row[0], row[1])
# 3) Add date to nodes for sorting (FIXME: how can we avoid this query???)
"""
for node in dg.nodes():
cursor.execute("SELECT snd.created, snd.original_filename, au.username " \
"FROM sounds_sound snd, auth_user au WHERE au.id=snd.user_id AND snd.id = %s", [node])
temp = cursor.fetchone()
dg.add_node(node, {'date':temp[0],
'nodeName': temp[1],
'username': temp[2]})
"""
dg = _create_nodes(dg)
# print dg.nodes(data=True)
# 4) Find weakly connected components (single direction)
subgraphs = nx.weakly_connected_component_subgraphs(dg)
node_list = []
# 5) delete all remixgroup objects to recalculate
RemixGroup.objects.all().delete()
# 6) Loop through all connected graphs in the dataset
# and create the groups
for sg in subgraphs:
# _create_and_save_remixgroup(sg_idx, sg)
_create_and_save_remixgroup(sg, RemixGroup())
"""
def handle(self, *args, **options):
# 1) Get all the sounds that have remixes
cursor = connection.cursor()
cursor.execute("""
SELECT
src.from_sound_id AS from,
src.to_sound_id AS to,
snd.created AS created
FROM
sounds_sound_sources src,
sounds_sound snd
WHERE
src.to_sound_id = snd.id
ORDER BY
snd.created ASC
""")
# 2) Create directed graph
dg = nx.DiGraph()
for row in cursor:
dg.add_edge(row[0], row[1])
# 3) Add date to nodes for sorting (FIXME: how can we avoid this query???)
"""
for node in dg.nodes():
cursor.execute("SELECT snd.created, snd.original_filename, au.username " \
"FROM sounds_sound snd, auth_user au WHERE au.id=snd.user_id AND snd.id = %s", [node])
temp = cursor.fetchone()
dg.add_node(node, {'date':temp[0],
'nodeName': temp[1],
'username': temp[2]})
"""
dg = _create_nodes(dg)
# print dg.nodes(data=True)
# 4) Find weakly connected components (single direction)
subgraphs = nx.weakly_connected_component_subgraphs(dg)
node_list = []
# 5) delete all remixgroup objects to recalculate
RemixGroup.objects.all().delete()
# 6) Loop through all connected graphs in the dataset
# and create the groups
for sg in subgraphs:
# _create_and_save_remixgroup(sg_idx, sg)
_create_and_save_remixgroup(sg, RemixGroup())
"""
def handle(self, *args, **options):
logger.info("Starting to create remix grooups")
# 1) Get all the sounds that have remixes
cursor = connection.cursor()
cursor.execute(
"""
SELECT
src.from_sound_id AS from,
src.to_sound_id AS to,
snd.created AS created
FROM
sounds_sound_sources src,
sounds_sound snd
WHERE
src.to_sound_id = snd.id
ORDER BY
snd.created ASC
"""
)
# 2) Create directed graph
dg = nx.DiGraph()
for row in cursor:
dg.add_edge(row[0], row[1])
# 3) Create nodes with dates and metadata
dg = _create_nodes(dg)
# 4) Find weakly connected components (single direction)
subgraphs = nx.weakly_connected_component_subgraphs(dg)
# 5) delete all remixgroup objects to recalculate
RemixGroup.objects.all().delete()
# 6) Loop through all connected graphs in the dataset and create the groups
n_groups_created = 0
for sg in subgraphs:
_create_and_save_remixgroup(sg, RemixGroup())
n_groups_created += 1
logger.info("Finished createing remix grooups (%i groups created)" % n_groups_created)
def handle(self, *args, **options):
logger.info("Starting to create remix grooups")
# 1) Get all the sounds that have remixes
cursor = connection.cursor()
cursor.execute("""
SELECT
src.from_sound_id AS from,
src.to_sound_id AS to,
snd.created AS created
FROM
sounds_sound_sources src,
sounds_sound snd
WHERE
src.to_sound_id = snd.id
ORDER BY
snd.created ASC
""")
# 2) Create directed graph
dg = nx.DiGraph()
for row in cursor:
dg.add_edge(row[0], row[1])
# 3) Create nodes with dates and metadata
dg = _create_nodes(dg)
# 4) Find weakly connected components (single direction)
subgraphs = nx.weakly_connected_component_subgraphs(dg)
# 5) delete all remixgroup objects to recalculate
RemixGroup.objects.all().delete()
# 6) Loop through all connected graphs in the dataset and create the groups
n_groups_created = 0
for sg in subgraphs:
_create_and_save_remixgroup(sg, RemixGroup())
n_groups_created += 1
logger.info("Finished createing remix grooups (%i groups created)" %
n_groups_created)
p_linkage_network = network_configuration_parameters["node"]["p"]
# sys.exit()
# create the random network where links rapresent truck link of the overall network
graph = nx.erdos_renyi_graph(num_node_network, p_linkage_network, directed=True)
output_folder = "data"
# assign to the random network edges the cost and capacity value of the truck
for s, d, data in graph.edges(data=True):
# transp = random.choice(type_of_transport)
data["capacity"] = type_of_transport["truck"]["capacity"]
data["cost"] = type_of_transport["truck"]["cost"]["mean"] + random.randint(-type_of_transport["truck"]["cost"]["variance"], type_of_transport["truck"]["cost"]["variance"])
largest_component = max(nx.weakly_connected_component_subgraphs(graph), key=len)
print len(largest_component)
print len(graph.edges())
# diameter = nx.diameter(largest)
nodes = largest_component.nodes()
print len(nodes)
# print diameter
diameter = 0
# create train arcs inside the random network of truck
for n1 in nodes:
tmp = list(nodes)
tmp.remove(n1)
for n2 in tmp:
try:
shortest_path_len = nx.shortest_path_length(largest_component, source=n1, target=n2)
if shortest_path_len > diameter:
